The general objective of this project is to consider the utility of mechanistic models of tumor development and detection in analysis of the impact of breast cancer screening in population- based settings. A stochastic model of cancer screening we propose offers the following distinct advantages: 1. It provides a simple but still realistic description of cancer latency; 2. It can be generalized in various ways while retaining its basic structure; 3. It furnishes a biologically meaningful interpretation of data analyses; 4. It accommodates standard population-based statistical data; its implementation does not depend heavily on availability of the data yielded by screening trials; 5. Rigorous statistical methods are available for estimating model parameters; 6. It can be used for designing optimal strategies of cancer screening and surveillance. The model will be validated with data on breast cancer from the Utah Population Data Base and the Utah Cancer Registry. Using these resources we will obtain initial parameter values for a pertinent estimation algorithm designed for grouped data on breast cancer mortality provided by the National Center for Health Statistics. This two-step estimation procedure will be tested by computer simulations and analyses of epidemiological data. In addition, we will explore the utility of stochastic approximation techniques in estimation of model parameters within the microsimulation framework.